The effects of events on time-stamped data may be an important factor in creating an accurate mathematical model of the data, for example to improve the predictive ability of the model. Examples of events that may affect time-stamped data include advertising campaigns, retail promotions, strikes, natural disasters, policy changes, a data recording error, etc. The characteristics of an event may define the effect that an occurrence has on the time-stamped data and the duration of the occurrence. An event may include a single occurrence (e.g., a worker strike), multiple occurrences (e.g., a holiday), or irregular occurrences (e.g., new store openings.) Further, an occurrence may be on a fixed date or a relative date (e.g., an event may begin two weeks before Christmas.) In addition, an event may be a combination of occurrences (e.g., a retail sale that occurs over a holiday.) Thus, modeling events so that they increase the accuracy of a time series model can often be complicated.
In accordance with the teachings described herein, systems and methods are provided for handling time-stamped data. The one or more GUIs may be used to define properties of an event and to associate the event with time-stamped data. The properties of the event defined using the one or more GUIs may include one or more statistical properties that indicate how the event statistically affects the time-stamped data. A time series analysis program may use the time-stamped data and the associated event to generate a mathematical model.